Prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) are exchange-traded markets created for the purpose of trading the outcome of events. The market prices can indicate what the crowd thinks the probability of the event is. A prediction market contract trades between 0 and 100%. It is a binary option that will expire at the price of 0 or 100%.

Research has suggested that prediction markets are at least as accurate as other institutions predicting the same events with a similar pool of participants.[1]

In 2001, Intrade.com launched a prediction market trading platform from Ireland allowing real money trading between members on contracts related to a number of different categories including business issues, current events, financial topics, and more. Intrade ceased trading in 2013.

The research literature is collected together in the peer reviewed The Journal of Prediction Markets, edited by Leighton Vaughan Williams and published by the University of Buckingham Press. The journal was first published in 2007, and is available online and in print.[4]

In October 2007 companies from the United States, Ireland, Austria, Germany, and Denmark formed the Prediction Market Industry Association, tasked with promoting awareness, education, and validation for prediction markets.

Some academic research has focused on potential flaws with the prediction market concept. In particular, Dr. Charles F. Manski of Northwestern University published "Interpreting the Predictions of Prediction Markets",[5] which attempts to show mathematically that under a wide range of assumptions the "predictions" of such markets do not closely correspond to the actual probability beliefs of the market participants unless the market probability is near either 0 or 1. Manski suggests that directly asking a group of participants to estimate probabilities may lead to better results.

However, Steven Gjerstad (Purdue) in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium",[6] has shown that prediction market prices are very close to the mean belief of market participants if the agents are risk averse and the distribution of beliefs is spread out (as with a normal distribution, for example). Justin Wolfers (Wharton) and Eric Zitzewitz (Dartmouth) have obtained similar results, and also include some analysis of prediction market data, in their paper "Interpreting Prediction Market Prices as Probabilities".[7] In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.[8][9]

Douglas Hubbard has also conducted a sample of over 400 retired claims which showed that the probability of an event is close to its market price but, more importantly, significantly closer than the average single subjective estimate.[3] However, he also shows that this benefit is partly offset if individuals first undergo calibrated probability assessment training so that they are good at assessing odds subjectively. The key benefit of the market, Hubbard claims, is that it mostly adjusts for uncalibrated estimates and, at the same time, incentivizes market participants to seek further information.

A series of laboratory experiments to compare the accuracy of prediction markets, traditional meetings, the Delphi method, and the nominal group technique on a quantitative judgment task, found only small differences between these four methods. Delphi was most accurate, followed by NGT and prediction markets. Meetings performed worst. The study also looked at participants' perceptions of the methods. Prediction markets were rated least favourable: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.[1]

A common belief among economists and the financial community in general is that prediction markets based on play money cannot possibly generate credible predictions. However, the data collected so far disagrees.[8] Analyzed data from the Hollywood Stock Exchange and the Foresight Exchange concluded that market prices predicted actual outcomes and/or outcome frequencies in the real world. Comparing an entire season's worth of NFL predictions from NewsFutures' play-money exchange to those of Tradesports, an equivalent real-money exchange based in Ireland, both exchanges performed equally well. In this case, using real money did not lead to better predictions.[9]

Prediction markets suffer from the same types of inaccuracy as other kinds of market, i.e. liquidity or other factors not intended to be measured are taken into account as risk factors by the market participants, distorting the market probabilities. Prediction markets may also be subject to speculative bubbles. For example, in the year 2000 IEM presidential futures markets, seeming "inaccuracy" comes from buying that occurred on or after Election Day, 11/7/00, but, by then, the trend was clear.

There can also be direct attempts to manipulate such markets. In the Tradesports 2004 presidential markets there was an apparent manipulation effort. An anonymous trader sold short so many Bush 2004 presidential futures contracts that the price was driven to zero, implying a zero percent chance that Bush would win. The only rational purpose of such a trade would be an attempt to manipulate the market in a strategy called a "bear raid". If this was a deliberate manipulation effort it failed, however, as the price of the contract rebounded rapidly to its previous level. As more press attention is paid to prediction markets, it is likely that more groups will be motivated to manipulate them. However, in practice, such attempts at manipulation have always proven to be very short lived. In their paper entitled "Information Aggregation and Manipulation in an Experimental Market" (2005),[10] Hanson, Oprea and Porter (George Mason U), show how attempts at market manipulation can in fact end up increasing the accuracy of the market because they provide that much more profit incentive to bet against the manipulator.

Using real-money prediction market contracts as a form of insurance can also affect the price of the contract. For example, if the election of a leader is perceived as negatively impacting the economy, traders may buy shares of that leader being elected, as a hedge.[11]

Some kinds of prediction markets may create controversial incentives. For example, a market predicting the death of a world leader might be quite useful for those whose activities are strongly related to this leader's policies, but it also might turn into an assassination market.[13]

The simExchange introduced a perpetual contract that it calls "stocks" to predict the global, lifetime sales of video game consoles and software titles. These stocks do not expire like most contracts on prediction markets because the founder, Brian Shiau, argued that video game sales can continue for years.[15] The premise for these stocks is that Shiau believes the video game industry suffers from a "lack of comprehensive sales data" and he compares the information problem of a game's sales to the information problem of evaluating a company's market value. Hanson warns that such a system may not work if a connection is not enforced.[16] Keith Gamble has described the simExchange as a Keynesian beauty contest[17] and that financial markets have certain remedies such as company buy-outs that cannot happen on the simExchange. Gamble concludes that such a prediction market can work but will be confined to play money.[18]

Best Buy, Motorola, Qualcomm, Edmunds.com, and Misys Banking Systems are listed as Consensus Point clients.[19]

Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units. Mentioned in academic publications from HP Labs. Also mentioned in Newsweek.[20] It is working towards a commercial launch of the implementation as a product, BRAIN (Behaviorally Robust Aggregation of Information Networks).[21]

France Telecom's Project Destiny has been in use since mid-2004 with demonstrated success.[23]

Google has confirmed in its official blog that it uses a predictive market internally.[24][25]

Novozymes applied prediction markets to an internal innovation contest that had the goal of identifying discontinuous product ideas. Besides accomplishing this goal, the initiative was successful in recombining ideas that had already been proposed by employees, but then ignored; it also supported R&D managers' evaluation by highlighting features of ideas otherwise overlooked. [26][27]

HSX built and operated a televised virtual stock market, the Interactive Music Exchange for Fuse Networks Fuse TV to be used as the basis of their daily live television broadcast, IMX, which ran from January, 2003 through July, 2004. The television audience traded virtual stocks of artists/videos/songs, and predicted which would make it to the top of the Billboard music charts. The first of its kind, Fuse Network and HSX won an AFI Enhanced TV (American Film Institute) Award for innoviation in television interactivity.[31]

Starwood embraced the use of prediction markets for developing and selecting marketing campaigns. Marketing department started out with some initial ideas and allowed employees to add new ideas or make changes to existing ones. Then subsequently incentives based prediction markets were leveraged to select the best of the lot.

A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes.[32] The advantage of making bets on combinations of outcomes is that, in theory, conditional information can be better incorporated into the market price.

One difficulty of combinatorial prediction markets is that the number of possible combinatorial trades scales exponentially with the number of normal trades. For example, a market with merely 100 binary contracts would have 2^100 possible combinations of contracts. These exponentially large data structures can be too large for a computer to keep track of, so there have been efforts to develop algorithms and rules to make the data more tractable.[33][34]

Since 2012, decentralized platforms for prediction markets have been in development. These platforms utilize blockchain technology and cryptocurrencies to provide various advantages over centralized markets, but also more challenges for regulators.[35] One such example is open-source software Augur.